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DEMO-EM2:通过链和结构域的交错拟合从冷冻电镜密度图组装蛋白质复合物结构。

DEMO-EM2: assembling protein complex structures from cryo-EM maps through intertwined chain and domain fitting.

机构信息

College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China.

Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.

出版信息

Brief Bioinform. 2024 Jan 22;25(2). doi: 10.1093/bib/bbae113.

Abstract

The breakthrough in cryo-electron microscopy (cryo-EM) technology has led to an increasing number of density maps of biological macromolecules. However, constructing accurate protein complex atomic structures from cryo-EM maps remains a challenge. In this study, we extend our previously developed DEMO-EM to present DEMO-EM2, an automated method for constructing protein complex models from cryo-EM maps through an iterative assembly procedure intertwining chain- and domain-level matching and fitting for predicted chain models. The method was carefully evaluated on 27 cryo-electron tomography (cryo-ET) maps and 16 single-particle EM maps, where DEMO-EM2 models achieved an average TM-score of 0.92, outperforming those of state-of-the-art methods. The results demonstrate an efficient method that enables the rapid and reliable solution of challenging cryo-EM structure modeling problems.

摘要

冷冻电镜(cryo-EM)技术的突破导致越来越多的生物大分子密度图的出现。然而,从 cryo-EM 图谱构建准确的蛋白质复合物原子结构仍然是一个挑战。在这项研究中,我们扩展了之前开发的 DEMO-EM 以呈现 DEMO-EM2,这是一种通过迭代组装过程将链和域级匹配和拟合相结合,从 cryo-EM 图谱构建蛋白质复合物模型的自动化方法,用于预测的链模型。该方法在 27 个冷冻电子断层扫描(cryo-ET)图谱和 16 个单颗粒 EM 图谱上进行了仔细评估,其中 DEMO-EM2 模型的平均 TM 评分达到 0.92,优于最先进的方法。结果表明该方法是一种高效的方法,能够快速可靠地解决具有挑战性的 cryo-EM 结构建模问题。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/89ec/10959074/7c69ed9e134e/bbae113f1.jpg

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